where \(\lambda \)  is a hyperparameter that balances two losses. The first loss represents the unconditional loss of GAN [1]}, and the second loss represents the conditional loss constrainted by the swelling cavities features. The loss \(\mathcal {L}_{H_v}\)  minimizes the difference between the cavities feature \(H^{\prime }_v\)  of the generated image \(X^{\prime }_{img}\)  and the true cavities feature \(H_v\) , defined as:\(\mathcal {L}_{H_v} = || H^{\prime }_v - H_v ||^2 = || En(X^{\prime }_{img}) - H_v ||^2\) .
